[ https://issues.apache.org/jira/browse/SPARK-40154?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean R. Owen resolved SPARK-40154. ---------------------------------- Fix Version/s: 3.5.1 4.0.0 3.4.2 Resolution: Fixed Issue resolved by pull request 43229 [https://github.com/apache/spark/pull/43229] > PySpark: DataFrame.cache docstring gives wrong storage level > ------------------------------------------------------------ > > Key: SPARK-40154 > URL: https://issues.apache.org/jira/browse/SPARK-40154 > Project: Spark > Issue Type: Bug > Components: PySpark > Affects Versions: 3.3.0 > Reporter: Paul Staab > Assignee: Paul Staab > Priority: Minor > Labels: pull-request-available > Fix For: 3.5.1, 4.0.0, 3.4.2 > > > The docstring of the `DataFrame.cache()` method currently states that it uses > a serialized storage level > {code:java} > Persists the :class:`DataFrame` with the default storage level > (`MEMORY_AND_DISK`). > [...] > - The default storage level has changed to `MEMORY_AND_DISK` to match > Scala in 2.0.{code} > while `DataFrame.persist()` states that it uses a deserialized storage level > {code:java} > If no storage level is specified defaults to (`MEMORY_AND_DISK_DESER`) > [...] > The default storage level has changed to `MEMORY_AND_DISK_DESER` to match > Scala in 3.0.{code} > > However, in practice both `.cache()` and `.persist()` use deserialized > storage levels: > {code:java} > import pyspark > from pyspark.sql import SparkSession > from pyspark import StorageLevel > print(pyspark.__version__) > # 3.3.0 > spark = SparkSession.builder.master("local[2]").getOrCreate() > df = spark.createDataFrame(zip(["A"] * 1000, ["B"] * 1000), ["col_a", > "col_b"]) > df = df.cache() > df.count() > # Storage level in Spark UI: "Disk Memory Deserialized 1x Replicated" > df = spark.createDataFrame(zip(["A"] * 1000, ["B"] * 1000), ["col_a", > "col_b"]) > df = df.persist() > df.count() > # Storage level in Spark UI: "Disk Memory Deserialized 1x Replicated" > df = spark.createDataFrame(zip(["A"] * 1000, ["B"] * 1000), ["col_a", > "col_b"]) > df = df.persist(StorageLevel.MEMORY_AND_DISK) > df.count() > # Storage level in Spark UI: "Disk Memory Serialized 1x Replicated"{code} > > -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org